Overview

Dataset statistics

Number of variables15
Number of observations7211
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory845.2 KiB
Average record size in memory120.0 B

Variable types

Numeric15

Alerts

TIME is highly correlated with S and 13 other fieldsHigh correlation
S is highly correlated with TIME and 13 other fieldsHigh correlation
T1 is highly correlated with TIME and 13 other fieldsHigh correlation
T2 is highly correlated with TIME and 13 other fieldsHigh correlation
T3 is highly correlated with TIME and 13 other fieldsHigh correlation
T4 is highly correlated with TIME and 13 other fieldsHigh correlation
T5 is highly correlated with TIME and 13 other fieldsHigh correlation
T6 is highly correlated with TIME and 13 other fieldsHigh correlation
T7 is highly correlated with TIME and 13 other fieldsHigh correlation
T8 is highly correlated with TIME and 13 other fieldsHigh correlation
T9 is highly correlated with TIME and 13 other fieldsHigh correlation
T10 is highly correlated with TIME and 13 other fieldsHigh correlation
T11 is highly correlated with TIME and 13 other fieldsHigh correlation
T12 is highly correlated with TIME and 13 other fieldsHigh correlation
Z is highly correlated with TIME and 13 other fieldsHigh correlation
TIME is uniformly distributed Uniform
TIME has unique values Unique
S has 1533 (21.3%) zeros Zeros

Reproduction

Analysis started2022-11-11 03:26:30.506414
Analysis finished2022-11-11 03:26:43.038469
Duration12.53 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

TIME
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct7211
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.4166667
Minimum0
Maximum600.8333333
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:26:43.065343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.04166667
Q1150.2083333
median300.4166667
Q3450.625
95-th percentile570.7916667
Maximum600.8333333
Range600.8333333
Interquartile range (IQR)300.4166667

Descriptive statistics

Standard deviation173.4817273
Coefficient of variation (CV)0.5774703823
Kurtosis-1.2
Mean300.4166667
Median Absolute Deviation (MAD)150.25
Skewness-2.02728154 × 10-16
Sum2166304.583
Variance30095.90972
MonotonicityStrictly increasing
2022-11-11T11:26:43.122156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
403.66666671
 
< 0.1%
401.16666671
 
< 0.1%
401.08333331
 
< 0.1%
4011
 
< 0.1%
400.91666671
 
< 0.1%
400.83333331
 
< 0.1%
400.751
 
< 0.1%
400.66666671
 
< 0.1%
400.58333331
 
< 0.1%
Other values (7201)7201
99.9%
ValueCountFrequency (%)
01
< 0.1%
0.083333333331
< 0.1%
0.16666666671
< 0.1%
0.251
< 0.1%
0.33333333331
< 0.1%
0.41666666671
< 0.1%
0.51
< 0.1%
0.58333333331
< 0.1%
0.66666666671
< 0.1%
0.751
< 0.1%
ValueCountFrequency (%)
600.83333331
< 0.1%
600.751
< 0.1%
600.66666671
< 0.1%
600.58333331
< 0.1%
600.51
< 0.1%
600.41666671
< 0.1%
600.33333331
< 0.1%
600.251
< 0.1%
600.16666671
< 0.1%
600.08333331
< 0.1%

S
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10306.03744
Minimum0
Maximum20001
Zeros1533
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:26:43.176489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18000
median12001
Q314000
95-th percentile19999
Maximum20001
Range20001
Interquartile range (IQR)6000

Descriptive statistics

Standard deviation6188.454101
Coefficient of variation (CV)0.600468816
Kurtosis-0.7963008249
Mean10306.03744
Median Absolute Deviation (MAD)3998
Skewness-0.5427095009
Sum74316836
Variance38296964.16
MonotonicityNot monotonic
2022-11-11T11:26:43.221340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
01533
21.3%
120011236
17.1%
15999931
12.9%
14000804
11.1%
9998737
10.2%
8000677
9.4%
18000403
 
5.6%
20001271
 
3.8%
13999256
 
3.6%
7999242
 
3.4%
Other values (5)121
 
1.7%
ValueCountFrequency (%)
01533
21.3%
16541
 
< 0.1%
19251
 
< 0.1%
7999242
 
3.4%
8000677
9.4%
9998737
10.2%
120011236
17.1%
13999256
 
3.6%
14000804
11.1%
15999931
12.9%
ValueCountFrequency (%)
20001271
 
3.8%
19999105
 
1.5%
18000403
 
5.6%
1799813
 
0.2%
167841
 
< 0.1%
15999931
12.9%
14000804
11.1%
13999256
 
3.6%
120011236
17.1%
9998737
10.2%

T1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct63
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.11037304
Minimum22.8
Maximum25.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:26:43.276813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.8
5-th percentile23.5
Q123.5
median23.8
Q324.6
95-th percentile25.5
Maximum25.9
Range3.1
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.6711773761
Coefficient of variation (CV)0.02783770185
Kurtosis-0.02335884179
Mean24.11037304
Median Absolute Deviation (MAD)0.3
Skewness0.8868034638
Sum173859.9
Variance0.4504790703
MonotonicityNot monotonic
2022-11-11T11:26:43.333624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.51904
26.4%
23.8806
11.2%
23.9737
 
10.2%
24.6606
 
8.4%
24.5575
 
8.0%
23.6541
 
7.5%
25.1367
 
5.1%
24.7247
 
3.4%
24.9215
 
3.0%
25.9186
 
2.6%
Other values (53)1027
14.2%
ValueCountFrequency (%)
22.826
0.4%
22.851
 
< 0.1%
22.92
 
< 0.1%
22.951
 
< 0.1%
234
 
0.1%
23.051
 
< 0.1%
23.137
0.5%
23.151
 
< 0.1%
23.228
0.4%
23.251
 
< 0.1%
ValueCountFrequency (%)
25.9186
2.6%
25.852
 
< 0.1%
25.887
1.2%
25.752
 
< 0.1%
25.746
 
0.6%
25.652
 
< 0.1%
25.614
 
0.2%
25.552
 
< 0.1%
25.524
 
0.3%
25.452
 
< 0.1%

T2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.9629039
Minimum22.5
Maximum23.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:26:43.385449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.5
5-th percentile22.6
Q122.7
median22.8
Q323.2
95-th percentile23.5
Maximum23.5
Range1
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.313666252
Coefficient of variation (CV)0.0136596945
Kurtosis-0.9203276663
Mean22.9629039
Median Absolute Deviation (MAD)0.1
Skewness0.6422298268
Sum165585.5
Variance0.09838651767
MonotonicityNot monotonic
2022-11-11T11:26:43.424318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
22.72292
31.8%
23.51320
18.3%
22.8928
12.9%
23783
 
10.9%
23.1478
 
6.6%
22.9458
 
6.4%
23.2361
 
5.0%
22.5316
 
4.4%
23.4163
 
2.3%
22.699
 
1.4%
ValueCountFrequency (%)
22.5316
 
4.4%
22.699
 
1.4%
22.72292
31.8%
22.8928
12.9%
22.9458
 
6.4%
23783
 
10.9%
23.1478
 
6.6%
23.2361
 
5.0%
23.313
 
0.2%
23.4163
 
2.3%
ValueCountFrequency (%)
23.51320
18.3%
23.4163
 
2.3%
23.313
 
0.2%
23.2361
 
5.0%
23.1478
 
6.6%
23783
 
10.9%
22.9458
 
6.4%
22.8928
12.9%
22.72292
31.8%
22.699
 
1.4%

T3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.95017335
Minimum22.5
Maximum23.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:26:43.465746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.5
5-th percentile22.5
Q122.7
median22.8
Q323.1
95-th percentile23.5
Maximum23.5
Range1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.3153729455
Coefficient of variation (CV)0.01374163675
Kurtosis-0.8154943816
Mean22.95017335
Median Absolute Deviation (MAD)0.1
Skewness0.7236395273
Sum165493.7
Variance0.09946009477
MonotonicityNot monotonic
2022-11-11T11:26:43.523497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
22.72294
31.8%
23.51330
18.4%
22.81013
14.0%
22.9774
 
10.7%
23468
 
6.5%
23.1459
 
6.4%
22.5392
 
5.4%
23.2260
 
3.6%
23.4156
 
2.2%
22.657
 
0.8%
ValueCountFrequency (%)
22.5392
 
5.4%
22.657
 
0.8%
22.72294
31.8%
22.81013
14.0%
22.9774
 
10.7%
23468
 
6.5%
23.1459
 
6.4%
23.2260
 
3.6%
23.38
 
0.1%
23.4156
 
2.2%
ValueCountFrequency (%)
23.51330
18.4%
23.4156
 
2.2%
23.38
 
0.1%
23.2260
 
3.6%
23.1459
 
6.4%
23468
 
6.5%
22.9774
 
10.7%
22.81013
14.0%
22.72294
31.8%
22.657
 
0.8%

T4
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.25103314
Minimum22.8
Maximum23.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:26:43.635121image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.8
5-th percentile22.9
Q122.9
median23.2
Q323.5
95-th percentile23.8
Maximum23.8
Range1
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.3163022754
Coefficient of variation (CV)0.01360379444
Kurtosis-1.30476131
Mean23.25103314
Median Absolute Deviation (MAD)0.3
Skewness0.2321495208
Sum167663.2
Variance0.1000471294
MonotonicityNot monotonic
2022-11-11T11:26:43.677691image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
22.91868
25.9%
23.11114
15.4%
23.5738
 
10.2%
23.6728
 
10.1%
23.3596
 
8.3%
23.8549
 
7.6%
23.4519
 
7.2%
23.7384
 
5.3%
22.8326
 
4.5%
23.2253
 
3.5%
ValueCountFrequency (%)
22.8326
 
4.5%
22.91868
25.9%
23136
 
1.9%
23.11114
15.4%
23.2253
 
3.5%
23.3596
 
8.3%
23.4519
 
7.2%
23.5738
 
10.2%
23.6728
 
10.1%
23.7384
 
5.3%
ValueCountFrequency (%)
23.8549
 
7.6%
23.7384
 
5.3%
23.6728
 
10.1%
23.5738
 
10.2%
23.4519
 
7.2%
23.3596
 
8.3%
23.2253
 
3.5%
23.11114
15.4%
23136
 
1.9%
22.91868
25.9%

T5
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.2584385
Minimum23
Maximum23.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:26:43.719069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile23
Q123
median23.1
Q323.6
95-th percentile23.7
Maximum23.7
Range0.7
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.2739230628
Coefficient of variation (CV)0.01177736256
Kurtosis-1.484996456
Mean23.2584385
Median Absolute Deviation (MAD)0.1
Skewness0.4311199444
Sum167716.6
Variance0.07503384431
MonotonicityNot monotonic
2022-11-11T11:26:43.757689image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
233139
43.5%
23.61133
 
15.7%
23.7803
 
11.1%
23.4547
 
7.6%
23.1488
 
6.8%
23.3445
 
6.2%
23.2358
 
5.0%
23.5298
 
4.1%
ValueCountFrequency (%)
233139
43.5%
23.1488
 
6.8%
23.2358
 
5.0%
23.3445
 
6.2%
23.4547
 
7.6%
23.5298
 
4.1%
23.61133
 
15.7%
23.7803
 
11.1%
ValueCountFrequency (%)
23.7803
 
11.1%
23.61133
 
15.7%
23.5298
 
4.1%
23.4547
 
7.6%
23.3445
 
6.2%
23.2358
 
5.0%
23.1488
 
6.8%
233139
43.5%

T6
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.21184302
Minimum22.9
Maximum23.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:26:43.800292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.9
5-th percentile22.9
Q123
median23.2
Q323.4
95-th percentile23.6
Maximum23.6
Range0.7
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.2105170329
Coefficient of variation (CV)0.009069380346
Kurtosis-1.189053796
Mean23.21184302
Median Absolute Deviation (MAD)0.2
Skewness0.2715261933
Sum167380.6
Variance0.04431742112
MonotonicityNot monotonic
2022-11-11T11:26:43.840531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
231906
26.4%
23.41267
17.6%
23.2975
13.5%
23.1848
11.8%
23.5637
 
8.8%
23.3602
 
8.3%
22.9491
 
6.8%
23.6485
 
6.7%
ValueCountFrequency (%)
22.9491
 
6.8%
231906
26.4%
23.1848
11.8%
23.2975
13.5%
23.3602
 
8.3%
23.41267
17.6%
23.5637
 
8.8%
23.6485
 
6.7%
ValueCountFrequency (%)
23.6485
 
6.7%
23.5637
 
8.8%
23.41267
17.6%
23.3602
 
8.3%
23.2975
13.5%
23.1848
11.8%
231906
26.4%
22.9491
 
6.8%

T7
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.85656636
Minimum22.3
Maximum23.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:26:43.885127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.3
5-th percentile22.4
Q122.6
median22.7
Q323
95-th percentile23.5
Maximum23.5
Range1.2
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.3592364732
Coefficient of variation (CV)0.01571699212
Kurtosis-0.6283139029
Mean22.85656636
Median Absolute Deviation (MAD)0.1
Skewness0.7639671415
Sum164818.7
Variance0.1290508437
MonotonicityNot monotonic
2022-11-11T11:26:43.929948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
22.61939
26.9%
23.51279
17.7%
22.71017
14.1%
22.8772
 
10.7%
22.9506
 
7.0%
23466
 
6.5%
22.5428
 
5.9%
22.3312
 
4.3%
23.1197
 
2.7%
23.4149
 
2.1%
Other values (3)146
 
2.0%
ValueCountFrequency (%)
22.3312
 
4.3%
22.486
 
1.2%
22.5428
 
5.9%
22.61939
26.9%
22.71017
14.1%
22.8772
 
10.7%
22.9506
 
7.0%
23466
 
6.5%
23.1197
 
2.7%
23.214
 
0.2%
ValueCountFrequency (%)
23.51279
17.7%
23.4149
 
2.1%
23.346
 
0.6%
23.214
 
0.2%
23.1197
 
2.7%
23466
 
6.5%
22.9506
 
7.0%
22.8772
 
10.7%
22.71017
14.1%
22.61939
26.9%

T8
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.98786576
Minimum22.7
Maximum23.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:26:43.972059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.7
5-th percentile22.7
Q122.7
median22.9
Q323.3
95-th percentile23.4
Maximum23.4
Range0.7
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.2804761787
Coefficient of variation (CV)0.01220105344
Kurtosis-1.503404643
Mean22.98786576
Median Absolute Deviation (MAD)0.2
Skewness0.3503115119
Sum165765.5
Variance0.07866688683
MonotonicityNot monotonic
2022-11-11T11:26:44.010938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
22.72667
37.0%
23.41491
20.7%
22.9935
 
13.0%
23.2705
 
9.8%
23.1400
 
5.5%
22.8395
 
5.5%
23.3359
 
5.0%
23259
 
3.6%
ValueCountFrequency (%)
22.72667
37.0%
22.8395
 
5.5%
22.9935
 
13.0%
23259
 
3.6%
23.1400
 
5.5%
23.2705
 
9.8%
23.3359
 
5.0%
23.41491
20.7%
ValueCountFrequency (%)
23.41491
20.7%
23.3359
 
5.0%
23.2705
 
9.8%
23.1400
 
5.5%
23259
 
3.6%
22.9935
 
13.0%
22.8395
 
5.5%
22.72667
37.0%

T9
Real number (ℝ≥0)

HIGH CORRELATION

Distinct87
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.56684232
Minimum23.1
Maximum27.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:26:44.063760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.1
5-th percentile24
Q124.4
median25.6
Q326.55
95-th percentile27
Maximum27.4
Range4.3
Interquartile range (IQR)2.15

Descriptive statistics

Standard deviation1.061651031
Coefficient of variation (CV)0.04152452688
Kurtosis-1.258377238
Mean25.56684232
Median Absolute Deviation (MAD)1
Skewness-0.2145489395
Sum184362.5
Variance1.127102913
MonotonicityNot monotonic
2022-11-11T11:26:44.119572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.3611
 
8.5%
24.2476
 
6.6%
26.6416
 
5.8%
26.5372
 
5.2%
25.4367
 
5.1%
25.5346
 
4.8%
24.4337
 
4.7%
26.7320
 
4.4%
26.8319
 
4.4%
25.6288
 
4.0%
Other values (77)3359
46.6%
ValueCountFrequency (%)
23.121
0.3%
23.151
 
< 0.1%
23.28
 
0.1%
23.251
 
< 0.1%
23.319
0.3%
23.351
 
< 0.1%
23.436
0.5%
23.451
 
< 0.1%
23.541
0.6%
23.554
 
0.1%
ValueCountFrequency (%)
27.439
 
0.5%
27.354
 
0.1%
27.322
 
0.3%
27.254
 
0.1%
27.279
1.1%
27.156
 
0.1%
27.1129
1.8%
27.0516
 
0.2%
27186
2.6%
26.9520
 
0.3%

T10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct40
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.30285675
Minimum22.5
Maximum26.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:26:44.175512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.5
5-th percentile22.6
Q122.8
median23.2
Q323.6
95-th percentile25.4
Maximum26.4
Range3.9
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.7545351694
Coefficient of variation (CV)0.03237951371
Kurtosis6.874165278
Mean23.30285675
Median Absolute Deviation (MAD)0.4
Skewness2.44077015
Sum168036.9
Variance0.5693233219
MonotonicityNot monotonic
2022-11-11T11:26:44.229292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
22.6958
13.3%
23.4643
8.9%
22.9618
8.6%
23.6618
8.6%
22.7603
8.4%
23554
 
7.7%
23.1470
 
6.5%
23.2443
 
6.1%
23.7434
 
6.0%
23.3395
 
5.5%
Other values (30)1475
20.5%
ValueCountFrequency (%)
22.546
 
0.6%
22.6958
13.3%
22.7603
8.4%
22.8257
 
3.6%
22.9618
8.6%
23554
7.7%
23.1470
6.5%
23.2443
6.1%
23.3395
5.5%
23.4643
8.9%
ValueCountFrequency (%)
26.426
 
0.4%
26.3101
1.4%
26.239
 
0.5%
26.142
0.6%
2656
0.8%
25.938
 
0.5%
25.827
 
0.4%
25.711
 
0.2%
25.69
 
0.1%
25.56
 
0.1%

T11
Real number (ℝ≥0)

HIGH CORRELATION

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.19169325
Minimum22.2
Maximum24.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:26:44.281118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.2
5-th percentile22.4
Q122.7
median23.3
Q323.6
95-th percentile23.9
Maximum24.1
Range1.9
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.5235047296
Coefficient of variation (CV)0.02257294127
Kurtosis-1.335966311
Mean23.19169325
Median Absolute Deviation (MAD)0.5
Skewness-0.1643768534
Sum167235.3
Variance0.2740572019
MonotonicityNot monotonic
2022-11-11T11:26:44.327062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
22.5674
 
9.3%
23.3637
 
8.8%
23.6563
 
7.8%
23.7553
 
7.7%
22.6503
 
7.0%
23.8500
 
6.9%
23.5487
 
6.8%
23.9486
 
6.7%
22.7411
 
5.7%
23.2371
 
5.1%
Other values (10)2026
28.1%
ValueCountFrequency (%)
22.221
 
0.3%
22.3184
 
2.6%
22.4368
5.1%
22.5674
9.3%
22.6503
7.0%
22.7411
5.7%
22.8231
 
3.2%
22.9313
4.3%
23133
 
1.8%
23.1181
 
2.5%
ValueCountFrequency (%)
24.151
 
0.7%
24208
 
2.9%
23.9486
6.7%
23.8500
6.9%
23.7553
7.7%
23.6563
7.8%
23.5487
6.8%
23.4336
4.7%
23.3637
8.8%
23.2371
5.1%

T12
Real number (ℝ≥0)

HIGH CORRELATION

Distinct20
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.88520316
Minimum21.9
Maximum23.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:26:44.375510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21.9
5-th percentile22.2
Q122.5
median22.8
Q323.3
95-th percentile23.6
Maximum23.8
Range1.9
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.4518955572
Coefficient of variation (CV)0.01974618945
Kurtosis-1.097084422
Mean22.88520316
Median Absolute Deviation (MAD)0.4
Skewness0.1134768875
Sum165025.2
Variance0.2042095946
MonotonicityNot monotonic
2022-11-11T11:26:44.419425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
22.5939
13.0%
22.7782
10.8%
23.3701
9.7%
22.4678
9.4%
23.4541
 
7.5%
23.5458
 
6.4%
22.8396
 
5.5%
22.6391
 
5.4%
23369
 
5.1%
22.9299
 
4.1%
Other values (10)1657
23.0%
ValueCountFrequency (%)
21.933
 
0.5%
22119
 
1.7%
22.191
 
1.3%
22.2173
 
2.4%
22.3201
 
2.8%
22.4678
9.4%
22.5939
13.0%
22.6391
5.4%
22.7782
10.8%
22.8396
5.5%
ValueCountFrequency (%)
23.826
 
0.4%
23.7213
 
3.0%
23.6270
 
3.7%
23.5458
6.4%
23.4541
7.5%
23.3701
9.7%
23.2242
 
3.4%
23.1289
4.0%
23369
5.1%
22.9299
4.1%

Z
Real number (ℝ≥0)

HIGH CORRELATION

Distinct261
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.01856885
Minimum0
Maximum48.4
Zeros26
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:26:44.473220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.7
Q111.8
median19.4
Q325.2
95-th percentile43
Maximum48.4
Range48.4
Interquartile range (IQR)13.4

Descriptive statistics

Standard deviation10.70927375
Coefficient of variation (CV)0.5349670012
Kurtosis0.2631320318
Mean20.01856885
Median Absolute Deviation (MAD)7.3
Skewness0.8173289308
Sum144353.9
Variance114.6885442
MonotonicityNot monotonic
2022-11-11T11:26:44.530166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.9451
 
6.3%
6.7407
 
5.6%
7.2331
 
4.6%
11.8320
 
4.4%
13258
 
3.6%
20.2245
 
3.4%
11.3202
 
2.8%
25196
 
2.7%
39.1173
 
2.4%
48.4167
 
2.3%
Other values (251)4461
61.9%
ValueCountFrequency (%)
026
0.4%
0.851
 
< 0.1%
1.72
 
< 0.1%
2.551
 
< 0.1%
3.44
 
0.1%
3.621
0.3%
3.751
 
< 0.1%
4.16
 
0.1%
4.451
 
< 0.1%
4.612
0.2%
ValueCountFrequency (%)
48.4167
2.3%
47.919
 
0.3%
47.551
 
< 0.1%
47.270
1.0%
47.113
 
0.2%
47.051
 
< 0.1%
46.72
 
< 0.1%
46.614
 
0.2%
46.251
 
< 0.1%
46.22
 
< 0.1%

Interactions

2022-11-11T11:26:42.069876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:30.938682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.698779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.540612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.349607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.067342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.852122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.582543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.408519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.232220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.002753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.831298image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.544586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.395027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.253355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:42.116718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:30.989511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.749608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.590007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.397128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.116235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.900038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.633372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.458680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.284440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.052593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.878078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.596841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.445856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.311161image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:42.167617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.041604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.808154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.642110image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.446609image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.167411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.951313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.686365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.511809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.346232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.105594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.927575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.652919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.498760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.371979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:42.215429image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.090468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.860838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.693264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.494061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.215250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.000365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.736695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.562594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.407028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.158381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.975456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.769838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.549566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.431947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:42.261322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.137541image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.970800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.739935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.540239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.260612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.046210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.849590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.610152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.458852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.206219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.020263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.818673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.598673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.484769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:42.306621image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.184471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.020486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.786191image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.585088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.305583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.092065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.897502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.658129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.509682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.254059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.066109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.867424image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.647546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.539585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:42.352467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.232535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.070971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.834030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.630867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.352365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.140249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.945979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.708011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.557521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.304073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.111954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.919028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.698375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.599383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:42.476754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.285251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.125251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.885927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.680698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.401224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.190213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.998763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.760275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.608568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.355898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.161191image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.973843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.750200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.658185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:42.526008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.336080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.177888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.936477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.730269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.450741image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.241307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.051285image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.813097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.658461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.474499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.210577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.027715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.803175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.711007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:42.571781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.384920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.227666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.984454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.777112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.562655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.288150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.100065image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.861706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.706666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.523334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.257706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.078661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.852794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.761352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:42.621700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.441729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.280535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.036772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.826772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.612565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.338152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.153357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.913498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.756548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.576156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.306542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.133476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.905073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.814174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:42.666193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.492560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.328771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.083072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.872091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.655863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.383864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.201202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.961359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.801804image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.623677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.351467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.183411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.952960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.862214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:42.716768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.547373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.383655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.135894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.922588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.706603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.436029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.255069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.015360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.854136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.677496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.401535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.238473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.078768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.915387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:42.766644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.599914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.437565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.250504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.972420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.756402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.485861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.307819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.131467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.904695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.730130image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.450392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.292292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.138566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.968170image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:42.815687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:31.651937image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:32.491777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:33.301769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.021531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:34.806315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:35.536268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:36.359608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.184481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:37.955273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:38.782516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:39.499296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:40.345868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:41.199390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:26:42.021097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-11T11:26:44.645739image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-11T11:26:44.719490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-11T11:26:44.796231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-11T11:26:44.873969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-11T11:26:44.950711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-11T11:26:42.894950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-11T11:26:43.001576image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TIMEST1T2T3T4T5T6T7T8T9T10T11T12Z
00.000000022.822.822.822.923.023.622.822.823.125.922.221.90.0
10.083333022.822.822.822.923.023.622.822.823.125.922.221.90.0
20.166667022.822.822.822.923.023.622.822.823.125.922.221.90.0
30.250000022.822.822.822.923.023.622.822.823.125.922.221.90.0
40.333333022.822.822.822.923.023.622.822.823.125.922.222.00.0
50.416667022.822.822.822.923.023.622.822.823.125.922.221.90.0
60.500000022.822.822.822.923.023.622.822.823.125.822.222.00.0
70.583333022.822.822.822.923.023.622.822.823.125.922.221.90.0
80.666667022.822.822.822.923.023.622.822.823.125.822.221.90.0
90.750000022.822.822.822.923.023.622.822.823.125.922.221.90.0

Last rows

TIMEST1T2T3T4T5T6T7T8T9T10T11T12Z
7201600.083333023.523.523.523.523.623.623.523.425.4022.923.122.719.9
7202600.166667023.523.523.523.523.623.623.523.425.4022.923.222.719.9
7203600.250000023.523.523.523.523.623.623.523.425.4022.923.122.719.9
7204600.333333023.523.523.523.523.623.623.523.425.4022.923.122.719.9
7205600.416667023.523.523.523.523.623.623.523.425.4022.823.122.719.9
7206600.500000023.523.523.523.523.623.623.523.425.3522.923.122.719.9
7207600.583333023.523.523.523.523.623.623.523.425.3022.923.122.719.9
7208600.666667023.523.523.523.523.623.623.523.425.3022.923.122.719.9
7209600.750000023.523.523.523.523.623.623.523.425.3022.923.122.719.9
7210600.833333023.523.523.523.523.623.623.523.425.3022.823.122.719.9